Method and apparatus of determining product category information

ABSTRACT

A user may submit product title information to a server. The server may generate a phrase based on the product title information. The server may then search a database to find relevancies between the phrase and product categories corresponding to multiple nodes in a product category tree. Based on the relevancies, the server may select a node from the multiple nodes. The server may associate the product title information with the node corresponding to a product category when the node is a leaf node of the product category tree.

CROSS REFERENCE TO RELATED PATENT APPLICATIONS

This application is a continuation of and claims priority to U.S. patentapplication Ser. No. 13/518,267, filed on Jun. 21, 2012, which isnational stage application of an international patent applicationPCT/US12/31875, filed Apr. 2, 2012, which claims priority to ChinesePatent Application No. 201110093430.9, filed on Apr. 14, 2011, entitled“METHOD AND APPARATUS OF DETERMINING PRODUCT CATEGORY INFORMATION,”which applications are hereby incorporated by reference in theirentirety.

TECHNICAL FIELD

This disclosure relates to the field of data processing. Morespecifically, the disclosure relates to a method and an apparatus ofdetermining product category information.

BACKGROUND

When a user uploads information of products through a server, the userneeds to determine a category of the uploaded product according toproduct category information provided by the server, and associate thecategory with the information of the products. However, conventionaltechnologies of determining product category information may presentsome problems (e.g., inaccurate results and inefficiency). For example,it may be difficult to select proper category when the product categoryinformation provided by the server is complex.

SUMMARY OF THE DISCLOSURE

This disclosure provides methods and devices for determining productcategory information. In some embodiments, a user may submit producttitle information to a server. The server may generate a phrase based onthe product title information. The server may then search a database tofind relevancies between the phrase and product categories correspondingto multiple nodes in a product category tree. Based on the relevancies,the server may select a node from the multiple nodes. The server mayassociate the product title information with the node corresponding to aproduct category when the node is a leaf node of the product categorytree.

In some embodiments, the server may select a node of a product categorytree. The node may correspond to a product category that includesmultiple product titles. The server may generate a phrase based on aproduct title of the multiple product titles, and then calculate arelevancy between the phrase and the product category based onrelationship between the phrase and the multiple product titles.

BRIEF DESCRIPTION OF THE DRAWINGS

The Detailed Description is described with reference to the accompanyingfigures. The use of the same reference numbers in different figuresindicates similar or identical items.

FIG. 1 is a block diagram of an illustrative structure of an exampleproduct category tree.

FIG. 2 is a block diagram of an illustrative architecture to determineproduct category information.

FIG. 3 is a flow diagram of an illustrative process to determine productcategory information.

FIG. 4 is a flow diagram of an illustrative process to calculaterelevancy between product information and product category.

FIG. 5 is a flow diagram of another illustrative process to determineproduct category information.

FIG. 6 is a block diagram of an illustrative computing device that maybe deployed in the environment shown in FIG. 2.

DETAILED DESCRIPTION

The disclosure introduces a method, a system and an apparatus ofdetermining product category information for improving accuracy andefficiency of searching product categories with which a product may beassociated. A relevancy between product information and correspondingproduct category information may be calculated and stored in a database.The product information may be provided by a user, and the correspondingproduct category information may be provided by a service provider. Asone example, the relevancy may be stored in the database asProduct_ID-Product_Title_Information-Product_Category_ID.

The product category may be a product category corresponding to a leafnode of a product category tree. In the product category tree, there arehigher level nodes of the leaf node (e.g., parent nodes), while thereare no lower level nodes of the leaf node (e.g., child nodes). Theproduct information may be associated with the product categorycorresponding to the leaf node. In addition, because of the relationshipbetween parent nodes and their child nodes, each corresponding node isdetermined from a root node to child nodes connected to the root node,and eventually to a corresponding leaf node during the determination ofthe product category. Therefore, in a product category tree, the productinformation may be associated with the leaf node, and product categoriescorresponding to parent nodes of the leaf node may include the productinformation.

FIG. 1 is a block diagram of an illustrative structure of an exampleproduct category tree 100. The product category tree 100 may include aroot node 102, which may include three child nodes: a plant gardeningnode 104, 3C digital node 106, and women's dress node 108, whichcorrespond to three product categories. The plant gardening node 104 mayfurther include two child nodes: a flower pot node 110 and a flower seednode 112. The flower pot node 110 may further include two child nodes: abulb flower node 114 and an aromatic flower node 116.

The bulb flower node 114 and the aromatic flower node 116 may be leafnodes, which correspond to product categories of bulb flowers andaromatic flowers respectively. Product information may be associatedwith product categories corresponding to the bulb flower node 114 andthe aromatic flower node 116. After the product information isassociated with a leaf node (e.g., the bulb flower node 114), associatedhigher-level nodes of this leaf node (e.g., the flower pot node 110 andplant gardening node 104) may include the product information as well.For example, a path from the leaf node to a particular associatedhigher-level node in the product category tree is reserved in thedatabase after the product information is associated with the leaf node.Therefore, a relevancy between the product information and associatedproduct category information reserved in the database can be shown as:Product_ID-Product_Title_Information-Associated_Root_Node_ID-Associated_Parent_Node_ID.

FIG. 2 is a block diagram of an illustrative architecture 200 todetermine product category information. Under the architecture 200, auser 202 may, via a user device 204, log in and provide productinformation to a server 206, which may provide a product category treeto the user 202. The user 202 may choose a product categorycorresponding to a leaf node by selecting children or grandchildrennodes of the root node, and then the server 206 may associate theproduct information to the product category. The server 206 can beimplemented as a web server. After the server 206 associate the productinformation to the product category, the server 206 may transmit relatedinformation to a database 208 for storage. In some embodiments, a cloudcomputing platform 210 may be implemented. For example, the cloudcomputing platform 210 may include Hadoop.

The cloud computing platform 210 may include a distributed analysissystem 212 and a real-time analysis system 214. In some embodiments, thecloud computing platform 210 may receive periodically the relatedinformation from the database 208. The cloud computing platform 210 maystore the product information and the associated product categoryinformation, analyze the information, and determine the relevancybetween the product information and the product category. For example,the cloud computing platform 210 may determine a relevancy between eachphase of the product information and each corresponding product categoryof the product category tree. In some embodiments, the distributedanalysis system 212 and the real-time analysis system 214 may beimplemented to determine the relevancy. The distributed analysis system212 may be an off-line distributed analysis system, and the real-timeanalysis system 214 may be an online real-time analysis system. Thecloud computing platform 210 may further provide information associatedwith relevancies to the server 206 to determine the product categoryinformation.

FIG. 3 is a flow diagram of an illustrative process 300 to determineproduct category information. At 302, the server 206 may receive productinformation, which may be submitted by the user 202. The productinformation may include product title information and other descriptioninformation of products. In some embodiments, the server 206 may receivethe product information from other servers while incorporating theproduct information in other servers into the database.

At 304, the server 206 may conduct word segmentation on the producttitle information to determine each phrase including one or more wordsegments and corresponding to the product title. In some embodiments,the word segment may be a word and/or character, or multiple wordsand/or characters. At 306, the server 206 may search a relevant valuebetween each phrase and each child node (e.g., the plant gardening node104, the 3C digital node 106 and the woman's dress node 108) of a rootnode (e.g., the root node 102). The server 206 may determine relevanciesbetween phrases and product categories based on relevant values betweenphrases and each child node. Therefore, the server 206 may determine arelevancy between each phrase and each product category.

At 308, the server 206 may determine a particular node corresponding tothe product information based on the relevancy between each phrase andeach child node. In some embodiments, the server 206 may select childnodes that have relevant values regarding a certain phrase grater than apredetermined relevant value. In some embodiments, the server 206 mayfurther determine a sum relevancy between all phrases derived from theproduct title information and a product category corresponding to eachselected child node. In some embodiments, the server 206 may select aproduct category corresponding to the product title information suchthat the product category has the greatest sum relevancy than otherproduct category.

At 310, the server 206 may set a child node of the root node thatcorresponds to the product category as a parent node. The server 206 maythen associate products corresponding to the product title informationto a product category corresponding to a leaf node derived from theparent node.

FIG. 4 is a flow diagram of an illustrative process 400 to calculaterelevancy between product information and product category. At 402, theserver 206 may determine a parent node corresponding to a node of eachproduct category. In some embodiments, a product category may be thecategory to which each product belongs. Each product category maycorrespond to a node of a product category tree, and the node with whichthe product information associated with may be the leaf node. In theproduct category tree 100, starting from the root node of the productcategory tree, the nodes are connected with each other. The root nodehas child nodes, leaf nodes only have parent nodes, and middle nodeshave parent nodes and child nodes.

At 404, the server 206 may determine phrases corresponding to producttitle information of each produce included in the node. In someembodiments, the server 206 may conduct word segmentation on the producttitle information to determine one or more phrases corresponding to theproduct title. Each of the phrases may include one or more wordsegments. For each phrase, the server 206 may determine a relevant valuebetween the phrase and the parent node based on a number that the phraseis contained in product titles included in the product categorycorresponding to the parent node. The relevant value may determinedbased further on a number that the phrase is contained in product titlesincluded in the product category corresponding to the node, and numbersof products included in product categories corresponding to the node andthe parent node respectively.

For example, the server 206 may determine a weighting of the phrase andthe corresponding node based on the number that the phrase is containedin product titles included in the product category corresponding to thenode, and a number of products included in a product categorycorresponding to the node. The server 206 may determine anotherweighting of the phrase and other product categories corresponding tothe parent node based on numbers of products included in productcategories corresponding to the node and the parent node respectively.Based on the two weightings, the server 206 may calculate the relevantvalue between the phrase and the corresponding product category. Foreach node, the relevant value between each phrase of the product titleinformation and the corresponding product category may be stored.

For example, to determine a relevancy between each phrase and the flowerpot 110, the server 206 may determine the plant gardening 104 as theparent node. For product title information included in the flower pot110, the server 206 may calculate the relevant value between each phraseof the product title information and the flower pot 110. To determine arelevancy between each phrase and the plant gardening 104, the server206 may identify the parent node of the plant gardening 104 as the rootnode 102. For product title information of each product included in theplant gardening node 104, the server 206 may calculate the relevantvalue between each phrase and the plant gardening 104. In someembodiments, the server 206 may calculate relevant values starting fromleaf nodes to the root node or from the root node to the leaf nodes.

Suppose that a product with an ID “1000” is associated with the bulbflower node 114. As results, according to the product category tree 100,the bulb flower node 114 is the leaf node, and the flower pot node 110is its parent node. Further suppose that the title information of theproduct 1000 is “Potable Hydroponics Purple Hyacinth Bulbs.” As results,the server 206 may conduct word segmentation on the title information,generate phrases based on “Hyacinth.” and determine a relevant valuebetween phrases including “Hyacinth” and the bulb flower node 114.

Further suppose that the number of products related to the productcategory corresponding to the flower pot node 110 is 1,623,912 and thenumber of products related to the product category corresponding to thebulb flower node 114 is 104,286. In addition, the number of occurrencesof the phrase “hyacinth” in the product title information of products ofthe bulb flower node 114 is 6,588, and the number in the product titleinformation of products of the flower pot node 110 is 20,683. Asresults, a weighting of the product category of the bulb flower node 114regarding the phrase “hyacinth” is 6.3% (i.e., 6588/104286). Anotherweighting of other products of the flower pot lode 110 is 0.1% (i.e.,(20683-6588)/0623912-104286)). Therefore the relevant value between thephrase “hyacinth” and the product category of the bulb flower 114 is85.3%, [6588/104286-(20683-6588)/(1623912-104286)]/(6588/104286)).Similarly, the server 206 may determine the relevant value between thephrase “hyacinth” and the product category of the flower pot node 110.

In some embodiments, the phrase may include multiple word segments(e.g., “Purple Hyacinth” or “Hyacinth Hyacinth Bulbs”). For example, forthe digital product and the digital product accessory of 3C electronics,relevancies between different words of product title information anddifferent product categories may be similar. It may be difficult todetermine to which product category a product belongs based on a singleword segment for each product category. For example, for the producttitle information including “genuine Samsung Galaxy S i9000 mobile phonebattery”, it can determine that a relevancy between the product category“mobile phone” and the product information, and another relevancybetween the product category “mobile phone battery” and the productinformation, are substantially similar. To solve the problem, the server206 may determine a relevancy between a phrase comprising at least twoword segments for each product category. For example, the server 206 maydetermine a relevancy between the phrase “mobile phone battery” and theproduct category “mobile phone battery.”

In some embodiments, a relevancy between a phrase comprising a wordsegment and each product category may be set as a one-order vector, anda relevancy between a phrase comprising at least two words and eachproduct category may be set as a two-order vector.

After determining and storing the relevant value between each phrase andeach product category, the server 206 may determine a product categorycorresponding to product title information that is provided by, forexample, the user 202.

FIG. 5 is a flow diagram of another illustrative process 500 todetermine product category information. At 502, the server 206 mayreceive product information that may include product title information.For example, the user device 204 may transmit the product information tothe server 206, and request for product category determination.

At 504, the server 206 may determine word segments based on the receivedproduct title information. Each word segment may be used for productcategory determination. In some embodiments, the server 206 may unifythe product title information and determine a character standardcorresponding to each character of the product title information. Forexample, when the product information has both upper case letters andlower case letters, the server 206 may convert lower case letters uppercase letters if the standard is upper case. The server may then conductword segmentation on the product title information, and remove certainword segment based on a non-useable word segment table to obtain wordsegments for product category determination.

At 506, the server 206 may determine a segment relevancy between a wordsegment and each child node of the root node or a parent node. Theserver 206 may determine the segment relevancy based on stored relevantvalues between phrases and each child node.

At 508, the server 206 may determine whether the segment relevancy fallsbelow a predetermined threshold. If the segment relevancy is less thanthe threshold (i.e., the “YES” branch from 508), the server 206 maydetermine whether the word segment is the last word of the product titleinformation at 509. If the server 206 determine that the word segment isthe last word segment of the product title information (i.e., the “YES”branch from 510), the operations 504 to 510 may be performed by a loopprocess (via dashed line from the operation 510 that leads back to theoperation 504). The server 206 may select another word segment forproduct category determination. If the word segment is not the last wordsegment (i.e., the “NO” branch from 510), the server 206 may generate aphrase including the word segment and at least another word segmentafter the word segment at 512.

If the segment relevancy is not less than the threshold (i.e., the “NO”branch from 508), the server 206 may select child nodes corresponding toeach word segment at 514. The server may select child nodes that haverelevant values greater than a predetermine value regarding the wordsegment. In some embodiments, the server may select child nodes havingrelevant values greater than other child nodes regarding the wordsegment. In some embodiments, the server 206 may search a relevancyregarding the phrase generated from the operation 512, and thendetermine the child node.

At 516, the server 206 may calculate one or more sum relevancies for aproduct category corresponding to each selected child node and theproduct title information. In some embodiments, the server 206 maydetermine a first sum relevancy based on relevancies between each wordsegment of the product title information and the selected productcategory. In some embodiments, the server 206 may determine a second sumrelevancy based on relevancies between each phrase derived from the wordsegments and the selected product category.

At 518, the server 206 may determine a particular product categorycorresponding to the product title information based on sum relevancies.This particular product category may have the greatest sum relevancy. Insome embodiments, the server 206 may calculate a total sum relevancybased on the first sum relevancy and the second sum relevancy and theparticular product category may have the greatest total sum relevancy.

At 520, the server 206 may set the node corresponding to the particularproduct category as a parent node and then determine corresponding childnodes. The operations 506 to 520 may be performed by a loop process (viadashed line from the operation 520 that leads back to the operation 506)until the product title information is associated to a product categorycorresponding to a leaf node.

For example, suppose that product title information of a productcomprises word segments A, B, and C, and the product category settingnumber is 3. In addition, the relevance between each word segment andeach product category contained in the product title information isprovided in table 1 below.

R1 R2 R3 R4 R5 A 50% 83% 62% B 40% 20% 37% C 42% 57% 32% AB 20% 40% 30%AC 10% 30% 40%

Further suppose that the product categories corresponding to child nodescontained in the root node are R1, R2, R3, the relevance between theword A and product categories R1, R2 and R3 are determined as 50%, 83%and 62%, the relevance between the word B and product categories R1, R3and R5 are determined as 40%, 20% and 37%, and the relevance between theword C and product categories R1, R2 and R4 are determined as 42%, 57%and 32%. Suppose that the set relevance threshold is 70%.

Accordingly, the relevancy between the phrase constituted by word A andword B and the product categories R1, R2 and R5 are determined as 20%,40% and 30%, and the relevancy between the phrase constituted by word Aand word C and the product categories R1, R2 and R3 are determined as10%, 30% and 40%. Furthermore, the first sum relevancy between theproduct title information and the product category R1 is 132%, the firstsum relevancy between the product title information and the productcategory R2 is 140%, the first sum relevancy between the product titleinformation and the product category R3 is 82%, the first sum relevancybetween the product title information and the product category R4 is32%, and the first sum relevancy between the product title informationand the product category R5 is 37%.

In addition, the second sum relevancy between the product titleinformation and the product category R1 is 30%, the second sum relevancybetween the product title information and the product category R2 is70%, the second sum relevancy between the product title information andthe product category R3 is 40%, and the second sum relevancy between theproduct title information and the product category R5 is 30%.

Accordingly, the sum relevancy between the product title information andthe product category R1 is 162%; the sum relevancy between the producttitle information and the product category R2 is 210%; the sum relevancybetween the product title information and the product category R3 is122%; the sum relevancy between the product title information and theproduct category R4 is 32%; and the sum relevancy between the producttitle information and the product category R5 is 67%. Therefore, thenode comprising the product information is the node corresponding to theproduct category R2. The server 206 may then set the node as a parentnode, determine child nodes of the parent node, and determines anothernode of the newly determined child nodes corresponding to the producttitle information until the product title information is associated to aproduct category corresponding to a leaf node.

FIG. 6 is a block diagram of an illustrative computing device 600 thatmay be deployed in the environment shown in FIG. 2. The server 206 maybe configured as any suitable server(s). In one exemplary configuration,the server 206 include one or more processors 602, input/outputinterfaces 604, network interface 606, and memory 608.

The memory 608 may include computer-readable media in the form ofvolatile memory, such as random-access memory (RAM) and/or non-volatilememory, such as read only memory (ROM) or flash RAM. The memory 608 isan example of computer-readable media.

Computer-readable media includes volatile and non-volatile, removableand non-removable media implemented in any method or technology forstorage of information such as computer readable instructions, datastructures, program modules, or other data. Examples of computer storagemedia include, but are not limited to, phase change memory (PRAM),static random-access memory (SRAM), dynamic random-access memory (DRAM),other types of random-access memory (RAM), read-only memory (ROM),electrically erasable programmable read-only memory (EEPROM), flashmemory or other memory technology, compact disk read-only memory(CD-ROM), digital versatile disks (DVD) or other optical storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or any other non-transmission medium that canbe used to store information for access by a computing device. Asdefined herein, computer-readable media does not include transitorymedia such as modulated data signals and carrier waves.

Turning to the memory 608 in more detail, the memory 608 may store amode determination module 610, a control module 612, a relevancydetermination module 614, a segmentation module 616, a searching module618, and a product category determination module 620. The nodedetermination module 610 may determine a node (e.g., child nodes andparent nodes) corresponding to a product within a product category tree.

The control module 612 may conduct word segmentation on the producttitle information and determine each phrase corresponding to producttitle information. The phrase may include at least a word segment. Foreach phrase, the control module 612 may determine a relevancy betweenthe phrase and a product category corresponding to the node according toan occurrence of the phrase in product title information contained in aproduct category corresponding to the corresponding parent node, anoccurrence of the phrase in product title information contained in aproduct category corresponding to the node, a number of the productcontained in a product category corresponding to the node and a numberof the product contained in a product category corresponding to theparent node.

In some embodiments, the server 206 may store relevance tables. Thetables may include a table having relevancies between each word and eachproduct category, and another table having relevancies between eachphrase constituted by at least two words and each product category. Eachphrase may include one or more word segments. The server 206 maydetermine the relevance between each phrase and each child nodeaccording to each child node connected to the root node and thepreserved relevance between each child node and each phrase. The server206 may determine the child node including the product title informationaccording to the relevance between each phrase of the product titleinformation and each child node. After that, the server 206 may set thechild node as a parent node and determine child nodes of the parentnode. Accordingly, the relevancy between each phrase of the producttitle information and each child node may be determined and the childnode containing the product title information may be determined untilthe product title information of a product is associated with to aproduct category corresponding to a leaf node.

In some embodiments, the control module 612 may determine a firstweighting between the phrase and a product category corresponding to thenode according to an occurrence of the phrase in product titleinformation contained in a product category corresponding to the nodeand a product number contained in a product category corresponding tothe node. The control module 612 may determine a second weightingbetween the phrase and a product category corresponding to the fathernode according to an occurrence of the phrase in product titleinformation contained in a product category corresponding to the fathernode, an occurrence of the phrase in product title information containedin a product category corresponding to the node, a number of the productcontained in a product category corresponding to the node and a numberof the product contained in a product category corresponding to theparent node. The control module 612 may determine a difference betweenthe first weighting and the second weighting, and determine therelevancy between the phrase and a product category corresponding to thenode according to a quotient of the difference and the first weighting.

In some embodiments, the control module 612 may determine the firstweight according to a quotient of an occurrence of the phrase in producttitle information contained in a product category corresponding to thenode and a product number contained in a product category correspondingto the node.

In some embodiments, the control module 612 may determine a firstdifference between an occurrence of the phrase in product titleinformation contained in a product category corresponding to the fathernode and an occurrence of the phrase in product title informationcontained in a product category corresponding to the node. The controlmodule 612 may also determine a second difference between a productnumber contained in a product category corresponding to the father nodeand a product number contained in a product category corresponding tothe node, and then determine the second weight according to a quotientof the first difference and the second difference.

In some embodiments, a relevancy between a phrase and a product categorymay be stored in the server 206. The server 206 may search and determinethe product category to which the product belongs by the on-linereal-time prediction system. The on-line on-time prediction system maybe included in the server 206. For example, the server 206 may includean Apache module. The Apache module may be implemented by the controlmodule 612 and the node determination 610 as well as the relevancydetermination module 614.

The relevancy determination module 614 may store relevancies betweenphrases and product categories corresponding to nodes of the productcategory tree. The segmentation module 616 may receive product titleinformation, conduct word segmentation on the product title informationand determine each phrase corresponding to the product titleinformation.

The searching module 618 may search relevancies between phrases andchild nodes according to relevant values between reserved phrases andproduct categories corresponding to nodes. The product categorydetermination module may determine a node containing the productinformation according to searched relevancies between phrases and childnodes, and set a child node as a parent node.

In some embodiments, the searching module 618 may determine a child nodecorresponding to each product category contained in the phrase accordingto the relevancy between each phrase reserved and each product category.The search module 618 may set the relevance between the stored phraseand a product category corresponding to the child node as a relevancybetween the searched phrase and the child node. In some embodiments, thesearching module 618 may determine whether the searched relevancebetween the phrase and the child node is greater than a preset relevancethreshold.

The product category determination module 620 may determine a child nodeof the parent node until the product title information of a product isassociated with a product category corresponding to a leaf node. In someembodiments, the product category determination module 620 may selectchild nodes having relevancies greater than a predetermined valuecorresponding to the searched phrase. The product category determinationmodule 620 may determine a sum relevancy between the product titleinformation and the product category based on a relevancy between eachphrase of the product title information and the product category. Theproduct category determination module 620 may determine a productcategory having the greatest sum relevance as a product categorycorresponding to a node containing the product information.

The embodiments in this disclosure are merely for illustrating purposesand are not intended to limit the scope of this disclosure. A personhaving ordinary skill in the art would be able to make changes andalterations to embodiments provided in this disclosure. Any changes andalterations that persons with ordinary skill in the art would appreciatefall within the scope of this disclosure.

1. One or more computer-readable media storing computer-executableinstructions that, when executed by one or more processors, cause theone or more processors to perform acts comprising: receiving productinformation associated with a product; conducting word segmentation onthe product information to generate a phrase that includes one or moreword segments; finding relevancies between the phrase and productcategories corresponding to multiple nodes having one parent node in aproduct category tree, each of the multiple nodes corresponding to oneproduct category; and selecting a node from the multiple nodes based onthe relevancies.
 2. The one or more computer-readable media of claim 1,wherein the one parent node is a root node of the product category tree.3. The one or more computer-readable media of claim 1, wherein the actsfurther comprise: determining whether the node is a leaf node of theproduct category tree; in the event that the node is the leaf node:associating the product information with a product categorycorresponding to the node, the product information including titleinformation of the product; in the event that the node is not the leafnode: determining that the node has one or more leaf nodes; determininga leaf node of the one or more leaf nodes based on relevancies betweenthe phrase and product categories corresponding to the one or more leafnodes; and associating the title information with a product categorycorresponding to the leaf node.
 4. The one or more computer-readablemedia of claim 3, wherein the acts further comprise: in the event thatthe node is not the leaf node: determining that the node does not have aleaf node; and determining one or more child nodes of the node.
 5. Theone or more computer-readable media of claim 1, wherein the acts furthercomprise: determining whether the relevancies are less than apredetermined threshold; in the event that the relevancies are less thanthe predetermined threshold, generating another phrase based on the oneor more segments; and finding other relevancies between the other phraseand the product categories corresponding to the multiple nodes.
 6. Theone or more computer-readable media of claim 1, wherein the selectingthe node comprises: selecting one or more nodes from the multiple nodes,the one or more nodes corresponding to product categories that haverelevancies with the phrase greater than a predetermined value;calculating one or more sum relevancies between the phrase and theproduct categories corresponding to the one or more nodes; and selectingthe node from the one or more nodes based on the one or more sumrelevancies.
 7. The one or more computer-readable media of claim 6,wherein the one or more sum relevancies comprise a sum relevancy that iscalculated based on relevancies between each of word segments andproduct categories corresponding to the selected one or more nodes, theword segments being generated based on the product information.
 8. Theone or more computer-readable media of claim 6, wherein the one or moresum relevancies comprise a sum relevancy that is calculated based onrelevancies between each of one or more phrases and the productcategories corresponding to the selected one or more nodes, the one ormore phrases being generated based on the word segments.
 9. The one ormore computer-readable media of claim 1, wherein the acts furthercomprise: setting the node as a parent node; and determining one or morechild nodes of the parent node.
 10. A computer-implemented methodcomprising: receiving product information; forming a phrase includingone or more word segments that are generated based on the productinformation; determining one or more nodes of a product category tree,the one or more nodes connecting with one parent node and havingrelevancies associated with the phrase greater than a predeterminedthreshold; and determining a node of the one or more nodes based onrelevancies between each of the one or more word segments and productcategories corresponding to the one or more nodes.
 11. Thecomputer-implemented method of claim 10, wherein the determining thenode of the one or more nodes comprises determining the node of the oneor more nodes based further on each of one or more phrases and theproduct categories corresponding to the one or more nodes, the one ormore phrases being generated based on the one or more word segments. 12.The computer-implemented method of claim 10, further comprising:determining that the node has one or more leaf nodes; determining a leafnode of the one or more leaf nodes based on relevancies between thephrase and product categories corresponding to the one or more leafnodes; and associating the product information with a product categorycorresponding the leaf node.
 13. The computer-implemented method ofclaim 10, further comprising: determining that the node has no leafnode; and determining one or more child nodes of the node.
 14. Thecomputer-implemented method of claim 10, wherein the one parent node isa root node of the product category tree.
 15. A computer-implementedmethod comprising: determining a node and a parent node of the node thatare associated with a product category tree, the node corresponding to aproduct category that includes multiple product titles; conducting wordsegmentation on a product title of the multiple product titles togenerate a phrase including one or more word segments; and calculating arelevancy between the phrase and the product category based onrelationship between the phrase and the multiple product titles.
 16. Thecomputer-implemented method of claim 15, wherein calculating therelevancy comprises calculating based on a number that the phrase iscontained in the multiple product titles.
 17. The computer-implementedmethod of claim 15, wherein calculating the relevancy comprisescalculating based on: a number of the multiple product titles included;and a number of product titles that are included in a product categorycorresponding to a parent node of the node.
 18. The computer-implementedmethod of claim 15, wherein calculating the relevancy comprisescalculating based on a first weighting value that is calculated based ona number that the phrase is contained in the product category and anumber of the multiple product titles included in the product category,and based on a second weighting value that is calculated based on anumber that the phrase is contained in the product category and a numberof the multiple product titles included in the product category.
 19. Thecomputer-implemented method of claim 18, wherein the second weightingvalue is calculated based further on: a number that the phrase iscontained in a product category corresponding to a parent node of thenode; and a number of product titles that are included in a productcategory corresponding to a parent node of the node.
 20. Thecomputer-implemented method of claim 15, further comprising storing therelevancy in a database associated with a cloud computing platform.